Cancellous Bone Histomorphometry and Fractal Analysis: Automated Skeletonization for Quantitative Assessment of Trabecular Connectivity and Complexity
Palavras-chave:
Osso e Ossos, Doenças Ósseas, Osso Esponjoso, Histologia, FractaisResumo
Introduction: Bone trabeculae complexity can be assessed in histology through skeletonization by measuring structural parameters and fractal analysis. Objective: To develop computational codes for automated bone trabeculae skeletonization in histological images to extract trabeculae data and Fractal Dimension (FD). Material and Methods: Cancellous bone from healthy (HB) and pathological (PB) conditions were analyzed in Mallory trichrome (MT) and hematoxylin & eosin (H&E) stained histological sections. Digital images (10× magnification) included MT staining with HB (n=26) and PB (n=147) and H&E staining with HB (n=55) and PB (n=46). Computational codes were created to automate segmentation and skeletonization using thresholding binarization. MT code involved color splitting channels, while H&E code used automatic thresholding. Skeletonized structures were analyzed for the number of trabeculae (Tb.n), branches (Brch), junctions (Jc), and endpoints (End.P). FD was determined via box-counting. Results: Both computational codes retrieved a well-defined skeletonized structure representing the trabecula in all images. Excellent visual matching of the linear structure compared with the original image for both HB and PB were found. Tb.n was higher for PB in both conditions and stains analyzed (p=0.0001). FD for PB was lower than HB (p=0.02) only in the H&E staining. In the four situations analyzed, FD showed strong positive correlations with Brch and Jc and moderate negative correlations with Tb.n and End.P (p<0.05). Conclusion: Automated skeletonization was achieved using computational codes to assess trabeculae spatial organization, including fractal analysis. PB and HB differed in Tb.n and FD, which correlated with all microarchitectural parameters.
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Chavassieux P, Chapurlat R. Interest of Bone Histomorphometry in Bone Pathophysiology Investigation: Foundation, Present, and Future. Front Endocrinol (Lausanne) 2022; 13; DOI: 10.3389/FENDO.2022.907914.
Brent MB, Emmanuel T. Contemporary Advances in Computer-Assisted Bone Histomorphometry and Identification of Bone Cells in Culture. Calcif Tissue Int 2023; 112(1):1–12; DOI: 10.1007/s00223-022-01035-2.
Linhares CRB, Rabelo GD, Limirio PHJO, Venâncio JF, Silva IGR, Dechichi P. Automated bone healing evaluation: New approach to histomorphometric analysis. Microsc Res Tech 2022; DOI: 10.1002/JEMT.24188.
Saha PK. Characterization of trabecular bone plate–rod micro-architecture using skeletonization and digital topologic and geometric analysis. Skeletonization: Theory, Methods and Applications. 2017; 287–311; DOI: 10.1016/B978-0-08-101291-8.00012-2.
Legrand E, Audran M, Guggenbuhl P, Levasseur R, Chalès G, Baslé MF, Chappard D. Trabecular bone microarchitecture is related to the number of risk factors and etiology in osteoporotic men. Microsc Res Tech 2007 Nov; 70(11):952-9. DOI: 10.1002/jemt.20501.
Silva MEB, Santos HS, Ruhland L, Rabelo GD, Badaró MM. Fractal analysis of dental periapical radiographs: A revised image processing method. Oral Surg Oral Med Oral Pathol Oral Radiol. 2023; 135(5):669–77; DOI: 10.1016/j.oooo.2022.11.014.
Kato CN, Barra SG, Tavares NP, Amaral TM, Brasileiro CB, Mesquita RA, et al. Use of fractal analysis in dental images: a systematic review. Dentomaxillofac Radiol. 2020; 49(2):20180457; DOI: 10.1259/dmfr.20180457.
Irie MS, Rabelo GD, Spin-Neto R, Dechichi P, Borges JS, Soares PBF. Use of Micro-Computed Tomography for Bone Evaluation in Dentistry. Braz Dent J. 2018; 29(3):227–38; DOI: 10.1590/0103-6440201801979.
Geraets WG, van der Stelt PF. Fractal properties of bone. Dentomaxillofac Radiol. 2000; 29(3):144–53; DOI: 10.1038/sj/dmfr/4600524.
Chappard D, Legrand E, Haettich B, Chalès G, Auvinet B, Eschard JP, et al. Fractal dimension of trabecular bone: comparison of three histomorphometric computed techniques for measuring the architectural two-dimensional complexity. J Pathol. 2001; 195(4):515–21; DOI: 10.1002/PATH.970.
Fazzalari NL, Parkinson IH. Fractal dimension and architecture of trabecular bone. J Pathol. 1996; 178(1):100–05; DOI: 10.1002/(SICI)1096-9896(199601)178:1<100::AID-PATH429>3.0.CO;2-K.
Bianciardi G. Differential Diagnosis: Shape and Function, Fractal Tools in the Pathology Lab [Internet]. Nonlinear Dynamics Psychol Life Sci. 2015 [cited 2025. Sep. 15]. Oct; 19(4):437-64. Available from: https://www.bing.com/search?qs=HS&pq=google+a&sk=CSYN1&sc=13-8&q=google+academico&cvid=e8f047b769734dcf8e96a0616f856432&gs_lcrp=EgRlZGdlKgkIABBFGDsY-QcyCQgAEEUYOxj5BzIGCAEQRRg5MgYIAhAuGEAyBggDEAAYQDIGCAQQABhAMgYIBRAuGEAyBggGEAAYQDIGCAcQRRg8MgYICBBFGDwyCAgJEOkHGPxV0gEIMjMwMmowajSoAgiwAgE&FORM=ANAB01&PC=U531.
Doube M, Klosowski MM, Arganda-Carreras I, et al. BoneJ: Free and extensible bone image analysis in ImageJ. Bone. 2010; 47(6):1076–79; DOI: 10.1016/J.BONE.2010.08.023.
Mukaka MM. Statistics corner: A guide to appropriate use of correlation coefficient in medical research [Internet]. Malawi Med J. 2012 [cited 2025. Sep. 15]. Sep; 24(3):69-71. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC3576830/.
Franciotti R, Moharrami M, Quaranta A, Bizzoca ME, Piattelli A, Aprile G, et al. Use of fractal analysis in dental images for osteoporosis detection: a systematic review and meta-analysis. Osteoporos Int. 2021; 32(6):1041–52; DOI: 10.1007/S00198-021-05852-3.
Tomazelli KB, Bianco BC, Castro BR, Ramos I, Zimmer VR, Soares PBF, et al. Morphology and spatial distribution of cortical bone canals: Evaluation of shape parameters, lacunarity, and fractal dimension in the human irradiated mandible. Bone. 2025; 192; DOI: 10.1016/j.bone.2024.117349.
Chappard D, Baslé MF, Legrand E, Audran M. Trabecular bone microarchitecture: A review. Morphologie. 2008; 92(299):162–70; DOI: 10.1016/J.MORPHO.2008.10.003.
Araújo AS, Fernandes ABN, Maciel JVB, Netto JNS, Bolognese AN. New methodology for evaluating osteoclastic activity induced by orthodontic load. Journal of Applied Oral Science. 2015; 23(1):19–25; DOI: 10.1590/1678-775720140351.
van ’t Hof RJ, Rose L, Bassonga E, Daroszewska A. Open source software for semi-automated histomorphometry of bone resorption and formation parameters. Bone. 2017; 99:69–79; DOI: 10.1016/J.BONE.2017.03.051.
Malhan D, Muelke M, Rosch S, Schaefer AB, Merboth F, Weisweiler D, et al. An Optimized Approach to Perform Bone Histomorphometry. Front Endocrinol (Lausanne). 2018; 9; DOI: 10.3389/FENDO.2018.00666.
Silva LM, Venâncio JF, Loures AO, Rabelo G, Lopes D, Dechichi P. Efeito do Diabetes Mellitus tipo I na organização espacial das trabéculas ósseas: análise por meio do processo de esqueletonização. HU Revista. 2019; 44(1):07–13; DOI: 10.34019/1982-8047.2018.v44.13926.
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Copyright (c) 2026 Thiago Pires Claudio, Daniela Oldra Zanotto, Matheus de Abreu, Riéli Elis Schulz, Daniel Caye, Rogério Oliveira Gondak, Gustavo Davi Rabelo
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